Re-weighting#
PolicyEngine-UK primarily relies on the Family Resources Survey, which has known issues with non-capture of households at the bottom and top of the income distribution. To correct for this, we apply a weight modification, optimised using gradient descent to minimise survey error against a diverse selection of targeting statistics. These include:
Regional populations
Household populations
Population by tenure type
Population by Council Tax band
Country-level program statistics
UK-wide program aggregates
UK-wide program caseloads
The graph below shows the effect of the optimisation on each of these, compared to their starting values (under original FRS weights). All loss subfunctions improve from their starting values.
Show code cell source
import pandas as pd
import numpy as np
import pandas as pd
import plotly.express as px
df = pd.read_csv(
"https://github.com/PolicyEngine/openfisca-uk-reweighting/raw/master/no_val_split/training_log_run_1.csv.gz",
)
ldf = (
df.groupby(["category", "epoch"])
.sum()
.reset_index()
.pivot(columns="category", values="loss", index="epoch")
)
ldf /= ldf.loc[0]
ldf -= 1
ldf = ldf.reset_index().melt(id_vars=["epoch"])
import plotly.express as px
ldf["hover"] = [
f"At epoch {epoch}, the total loss from targets <br>in the category <b>{category}</b> <br>has <b>{'risen' if value > 0 else 'fallen'}</b> by <b>{abs(value):.1%}</b>."
for epoch, category, value in zip(ldf.epoch, ldf.category, ldf.value)
]
px.line(
ldf, x="epoch", y="value", color="category", custom_data=[ldf.hover]
).update_traces(hovertemplate="%{customdata[0]}").update_layout(
title="Training performance by category",
height=600,
width=800,
xaxis_title="Epoch",
yaxis_title="Loss change",
legend_title="Category",
yaxis_range=(-1, 0),
yaxis_tickformat=".0%",
)
Changes to distributions#
Validation#
During initial training, we split the targets into training and validation groups (80%/20%), performing 5-fold cross-validation. The graph below shows the performance of validation metrics in each fold, as well as the average over the five folds.
Show code cell source
df = pd.read_csv(
"https://github.com/PolicyEngine/openfisca-uk-reweighting/raw/master/train_val_split/training_log.csv.gz",
compression="gzip",
)
xdf = pd.DataFrame()
for validation_type in (True, False, "Both"):
if isinstance(validation_type, bool):
condition = df.validation == validation_type
else:
condition = df.validation | ~df.validation
x = (
df[condition]
.groupby(["run_id", "epoch"])
.loss.sum()
.reset_index()
.pivot(columns="run_id", values="loss", index="epoch")
)
x /= x.loc[0]
x -= 1
x = x.dropna()
x["Average"] = x.mean(axis=1)
x["Type"] = {
True: "Validation",
False: "Training",
"Both": "Training + Validation",
}[validation_type]
xdf = pd.concat([xdf, x])
px.line(
xdf,
y=xdf.columns,
animation_frame="Type",
color_discrete_sequence=["lightgrey"] * 5 + ["grey"],
).update_layout(
title="5-fold cross-validation training",
yaxis_title="Relative loss change",
yaxis_tickformat=".0%",
xaxis_title="Epoch",
legend_title="Fold",
width=800,
height=800,
)
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[2], line 1
----> 1 df = pd.read_csv(
2 "https://github.com/PolicyEngine/openfisca-uk-reweighting/raw/master/train_val_split/training_log.csv.gz",
3 compression="gzip",
4 )
5 xdf = pd.DataFrame()
6 for validation_type in (True, False, "Both"):
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/pandas/io/parsers/readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
899 kwds_defaults = _refine_defaults_read(
900 dialect,
901 delimiter,
(...)
908 dtype_backend=dtype_backend,
909 )
910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/pandas/io/parsers/readers.py:577, in _read(filepath_or_buffer, kwds)
574 _validate_names(kwds.get("names", None))
576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
579 if chunksize or iterator:
580 return parser
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
1404 self.options["has_index_names"] = kwds["has_index_names"]
1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/pandas/io/parsers/readers.py:1661, in TextFileReader._make_engine(self, f, engine)
1659 if "b" not in mode:
1660 mode += "b"
-> 1661 self.handles = get_handle(
1662 f,
1663 mode,
1664 encoding=self.options.get("encoding", None),
1665 compression=self.options.get("compression", None),
1666 memory_map=self.options.get("memory_map", False),
1667 is_text=is_text,
1668 errors=self.options.get("encoding_errors", "strict"),
1669 storage_options=self.options.get("storage_options", None),
1670 )
1671 assert self.handles is not None
1672 f = self.handles.handle
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/pandas/io/common.py:716, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
713 codecs.lookup_error(errors)
715 # open URLs
--> 716 ioargs = _get_filepath_or_buffer(
717 path_or_buf,
718 encoding=encoding,
719 compression=compression,
720 mode=mode,
721 storage_options=storage_options,
722 )
724 handle = ioargs.filepath_or_buffer
725 handles: list[BaseBuffer]
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/pandas/io/common.py:373, in _get_filepath_or_buffer(filepath_or_buffer, encoding, compression, mode, storage_options)
370 if content_encoding == "gzip":
371 # Override compression based on Content-Encoding header
372 compression = {"method": "gzip"}
--> 373 reader = BytesIO(req.read())
374 return IOArgs(
375 filepath_or_buffer=reader,
376 encoding=encoding,
(...)
379 mode=fsspec_mode,
380 )
382 if is_fsspec_url(filepath_or_buffer):
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/http/client.py:476, in HTTPResponse.read(self, amt)
474 else:
475 try:
--> 476 s = self._safe_read(self.length)
477 except IncompleteRead:
478 self._close_conn()
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/http/client.py:626, in HTTPResponse._safe_read(self, amt)
624 s = []
625 while amt > 0:
--> 626 chunk = self.fp.read(min(amt, MAXAMOUNT))
627 if not chunk:
628 raise IncompleteRead(b''.join(s), amt)
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/socket.py:704, in SocketIO.readinto(self, b)
702 while True:
703 try:
--> 704 return self._sock.recv_into(b)
705 except timeout:
706 self._timeout_occurred = True
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/ssl.py:1242, in SSLSocket.recv_into(self, buffer, nbytes, flags)
1238 if flags != 0:
1239 raise ValueError(
1240 "non-zero flags not allowed in calls to recv_into() on %s" %
1241 self.__class__)
-> 1242 return self.read(nbytes, buffer)
1243 else:
1244 return super().recv_into(buffer, nbytes, flags)
File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/ssl.py:1100, in SSLSocket.read(self, len, buffer)
1098 try:
1099 if buffer is not None:
-> 1100 return self._sslobj.read(len, buffer)
1101 else:
1102 return self._sslobj.read(len)
KeyboardInterrupt:
The below chart visualises the effect of the training process on each individual training and validation metric, by epoch.
Show code cell source
df["rel_error"] = df.pred / df.actual - 1
df["Type"] = np.where(df.validation, "Validation", "Training")
STEP_SIZE = 50
cdf = df[df.epoch % STEP_SIZE == 0]
cdf = cdf[
(cdf.category == "Budgetary impact")
| (cdf.category == "UK-wide program aggregates")
]
fig = px.scatter(
cdf,
animation_frame="epoch",
x="actual",
y="rel_error",
color="Type",
hover_data=df.columns,
opacity=0.2,
)
layout = dict(
title="Target metrics",
width=800,
height=800,
legend_title="Type",
yaxis_title="Relative error",
yaxis_tickformat=".1%",
xaxis_tickprefix="£",
xaxis_title="Actual value",
yaxis_range=(-1, 1),
)
fig.update_layout(**layout)
for i, frame in enumerate(fig.frames):
frame.layout.update(layout)
frame.layout[
"title"
] = f"Budgetary impact target metric performance at {i * STEP_SIZE:,} epochs"
for step in fig.layout.sliders[0].steps:
step["args"][1]["frame"]["redraw"] = True
for button in fig.layout.updatemenus[0].buttons:
button["args"][1]["frame"]["redraw"] = True
import gif
import plotly.graph_objects as go
gif.save(
[
gif.frame(lambda: go.Figure(data=frame.data, layout=frame.layout))()
for frame in fig.frames
],
"scatterplot.gif",
duration=3_000 / len(fig.frames),
)
fig